Proximal Policy Optimization (PPO). [1]}. For both search spaces, we can formulate the NAS problem as a sequential decision problem, and the reinforcement learning algorithm PPO aims to select each attribute choice to form a network. We set the learning rate as 0.01, and it decays by 0.999 for every 15 steps. The optimizer is Adam. We set the clipping parameter \(\epsilon \)  as 0.2, memory size as 100, discount \(\gamma \)  as 0.99, GAE parameter \(\lambda \)  as 0.95, value function coefficient as 1, and entropy coefficient as 0.01. For PPO-transfer, we first pre-train the policy by applying the policy to search on the lowest cost tasks, jigsaw, then transfer to target tasks.
